BlastFunction is a serverless platform that brings FPGA acceleration capabilities for specific functions through heterogeneous computing. It enables resource sharing across multiple users to maximize FPGA utilization and minimize costs for cloud providers. BlastFunction manages functions and machines, redistributing workloads across nodes equipped with FPGAs. Initial results show BlastFunction improved FPGA utilization and increased performance per watt for benchmark applications compared to native CPU execution. Future work includes porting BlastFunction to AWS and automating cluster management.

1. BlastFunction
How to combine Serverless and FPGAs
Samuele Barbieri
July 29, 2020 @ Remote

2. Cloud Computing
2

3. A problem with CPUs
3
CPUs can be
computationally inefficient
in specific domains such as
neural networks, genomics
or financial.
Heterogeneous systems can
combine the best attributes
from more architectures, like
CPUs with FPGAs.

4. An underutilization issue
4
Not fully exploited FPGAs
translates into unprofitable
costs from the end-user
perspective.
Cloud providers can share
resources among multiple users
to increase utilization, optimizing
costs.

5. BlastFunction
5
BlastFunction brings
heterogeneous computing
capabilities through FPGA for the
acceleration of specific functions.
The Serverless paradigm
bring simplicity to users
while enabling resource
sharing to minimize
underutilization.

6. BlastFunction - Time Sharing Optimization
6
The same function is
anew instantiated for
each user, allocating
more resources.
time
reconfiguration time
Increasing FPGA’s utilization sharing the same device.
time
reconfiguration time

7. BlastFunction - Orchestration Of Functions
7
BLF
BlastFunction manages both functions and
machines, redistributing the workload across
available nodes.

8. 8
BlastFunction - Orchestration Of Functions
OpenFAAS operates as a gateway for the end-user
functions, while BlastFunction’s services and
functions’ docker containers run on a kubernetes
cluster, interfacing with nodes and FPGAs.

9. 9
Results
Worker Nodes
Intel i7-6700, 32GB RAM
Master Node
Intel Xeon-W3530, 24GB RAM
FPGA board (all nodes)
Terasic DE5a-Net Stratix V GX
Power Meter
WattsUp .Net, serial USB
Benchmark
Spector benchmark suite
Sobel accelerator
Utilization: 1.46x
Throughput: 1.40x
Latency: 1.04x
Perf/W: 1.25x
Matrix Mult. accelerator
Utilization: 1.17x
Throughput: 2.15x
Latency: 0.59x
Perf/W: 1.38x
BLF vs Native execution
Perf/W: up to 2.75x (Sobel) and 20x (MM)
Accelerated vs NOT Accelerated

10. 10
Future Works
BlastFunction migration
to AWS in order to test on
a larger number of nodes.
● Automate cluster creation and teardown.
● Adapt BLF to support Xilinx FPGAs.
● Horizontal scaling automation.

11. Thank you
Samuele Barbieri | samuele.barbieri@mail.polimi.it
Marco Bacis | marco.bacis@mail.polimi.it
Rolando Brondolin | rolando.brondolin@polimi.it
Marco Santambrogio | marco.santambrogio@polimi.it
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